کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4948477 | 1439613 | 2016 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Large-scale distance metric learning for k-nearest neighbors regression
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
This paper presents a distance metric learning method for k-nearest neighbors regression. We define the constraints based on triplets, which are built from the neighborhood of each training instance, to learn the distance metric. The resulting optimization problem can be formulated as a convex quadratic program. Quadratic programming has a disadvantage that it does not scale well in large-scale settings. To reduce the time complexity of training, we propose a novel dual coordinate descent method for this type of problem. Experimental results on several regression data sets show that our method obtains a competitive performance when compared with the state-of-the-art distance metric learning methods, while being an order of magnitude faster.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 214, 19 November 2016, Pages 805-814
Journal: Neurocomputing - Volume 214, 19 November 2016, Pages 805-814
نویسندگان
Bac Nguyen, Carlos Morell, Bernard De Baets,